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Ask AI: What is the most important question regarding AI transformations?

What is the scope of the AI transformation?

The most important questions that data transformation leaders should consider in terms of their company's AI transformation include understanding the scope of the AI transformation and how to drive skills transformation across the four groups of employees. To do so, leaders should translate business goals into skills that can be measured and observed. AI careers and digital transformation are two key components that should be considered when strategizing AI transformation. As AI continues to develop, leaders must remain informed on the latest AI technologies and trends to ensure the success of their AI transformation initiatives.

AI is changing the workforce as we know it. With AI becoming more integrated into organizations, the need for AI experts is increasing. However, many people are still unsure of what AI is and how they can become fluent in this technology.

5 tips on AI transformations

  • AI transformations can be difficult to execute, but data leaders should begin by segmenting their employees into four major groups: top practitioners, AI plus X experts, people who need to be fluent in AI, and people who need to understand AI. AI careers will become increasingly important as AI technology becomes more integrated into the organization.
  • Top practitioners are responsible for heavy lifting and the platform and infrastructure of the transformation. AI plus X experts are subject matter experts who need to acquire an AI toolkit to build applications related to their data or subject matter expertise.
  • People who need to be fluent in AI interact with technical practitioners and need to understand why AI is necessary for the company. Finally, people who need to be literate in AI need to understand how AI has evolved recently and why it is important for the company to shift its culture of adopting new technologies.
  • Leaders should set goals that are not easy to measure but can be translated into skills that can be measured. For example, they may want every decision and process to be informed by data, but this is difficult to observe without using skills data.
  • Leaders should use skills data to take action on business goals to execute an AI transformation within their organisation successfully.

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